cs.DB · 2026-08-11 · No. 81

Databases, 2026-08-11.

5 new papers in cs.DB. Titles, authors, abstracts. Links to arXiv. Want this in your inbox every morning? Subscribe →

01 — The papers

5 entries
  1. 01

    Carnot: Interpretable, Interactive, and Optimized Execution of Deep Research Queries

    Matthew Russo, Yash Agarwal, Tianyu Li, Zhuohan Gu, Michael Cafarella, Omar Khattab, Tim Kraska, Samuel Madden

    cs.DB · cs.AI

    Enterprises increasingly seek to query data lakes using natural language via AI-driven tools like semantic operators or deep research agents. However, the latter operates as an opaque black box, hiding its intermediate reasoning and data retrieval steps, and failing to expose controls for managing API costs and execution latency. Meanwhile, the former can be prohibitively expensive for enterprise-scale data lakes. Consequently, analysts using...

    arxiv.org/abs/2608.09532 · PDF

  2. 02

    Monotonicity-Guided Bottom-Up Petri Net Discovery: The SPECpp Framework

    Leah Tacke genannt Unterberg, Lisa L. Mannel, Wil M. P. van der Aalst

    cs.DB · cs.AI

    Process discovery is one of the central challenges in process mining. Petri nets are particularly attractive because simple local constructs can express complex behavior, including concurrency. While their global behavior may be difficult to analyze, individual places can be efficiently characterized using monotonic properties, enabling bottom-up discovery. Unlike top-down approaches such as the Inductive Miner, which rely on predefined...

    arxiv.org/abs/2608.09398 · PDF

  3. 03

    SafeQL: Search-based Refinement for Safe and Efficient LLM-based Text-to-SQL

    Geonho Lee, Min-Soo Kim

    cs.DB · cs.AI

    Large language models (LLMs) have advanced Text-to-SQL by enabling natural language interfaces to databases without task-specific fine-tuning. However, existing LLM-based systems remain unreliable, often generating SQL queries that are invalid under the database schema, referencing non-existent tables, attributes, functions, or values. Such errors persist because interactions with the database management system (DBMS) are typically limited to...

    arxiv.org/abs/2608.09260 · PDF

  4. 04

    AkasicDB: Demonstrating Omni RAG with a Unified Vector-Graph-Relational DBMS

    Geonho Lee, Jeongho Park, Donghyoung Han, Min-Soo Kim

    cs.DB · cs.AI

    Recent Retrieval-Augmented Generation (RAG) systems increasingly combine vector retrieval with structured knowledge, such as Graph RAG and Filtered vector search. However, existing database architectures struggle to support such complex RAG workflows efficiently, as they rely on out-of-DB pipelines or in-DB non-native integration, leading to high overhead. This demo paper presents AkasicDB, a database system that natively supports such RAG...

    arxiv.org/abs/2608.09214 · PDF

  5. 05

    SiriusDeliver: Automating Data Warehouse Delivery at Tencent

    Haining Xie, Xiaokai Zhou, Jiaming Yang, Siqi Shen, Ziwei Wang, Yifeng Zheng, Tengyue Xu, Yipeng Shi, Zefang Zong,...

    cs.DB · cs.AI · cs.SE

    Enterprise data warehouses (DWs) support business-critical analytics, but warehouse task delivery remains a complicated production process involving context retrieval, workflow configuration, code generation, platform submission, and failure diagnosis. Although large language models (LLMs) and coding agents have improved software development, they are insufficient for production DW delivery, which requires dependency-aware orchestration,...

    arxiv.org/abs/2608.09185 · PDF

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